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import gradio as gr
from huggingface_hub import InferenceClient
import json
import time
import random
class ChatAgent:
def __init__(self):
self.client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
self.load_content()
self.engagement_prompts = [
"👋 Hi there! Looking for an AI solution? We have options from $100 for students to enterprise-grade systems.",
"🎓 Are you a student? Check out our Student Study Assistant - lifetime access for just $100!",
"🚀 Ready to transform your workflow with AI? Our RAG Assistant Pro might be perfect for you.",
"🏢 Need an enterprise AI solution? Let's discuss your custom requirements.",
]
def load_content(self):
with open("data/site_content.json", "r") as f:
self.content = json.load(f)
def get_product_info(self, product_name=None):
products = self.content.get('products', [])
if product_name:
for product in products:
if product['name'].lower() == product_name.lower():
return product
return products[0] # Return first product if none specified
def generate_initial_greeting(self):
return random.choice(self.engagement_prompts)
def get_response(self, message, history):
# Get relevant product based on message content
context = ""
if "student" in message.lower():
product = self.get_product_info("Student Study Assistant")
context = f"Focusing on Student Study Assistant: {product['description']} Price: {product['price']}"
elif "rag" in message.lower() or "professional" in message.lower():
product = self.get_product_info("Personalized RAG Assistant Pro")
context = f"Focusing on RAG Assistant Pro: {product['description']} Price: {product['price']}"
elif "enterprise" in message.lower():
product = self.get_product_info("Enterprise AI Suite")
context = f"Focusing on Enterprise AI Suite: {product['description']}"
elif "custom" in message.lower() or "llm" in message.lower():
product = self.get_product_info("Custom LLM Platform")
context = f"Focusing on Custom LLM Platform: {product['description']}"
system_message = f"""You are a helpful sales assistant for Sletcher Systems.
Current product information: {context}
Style: Be friendly, professional, and helpful. Focus on understanding the customer's needs.
Goals: Help customers find the right AI solution and encourage them to schedule a consultation.
"""
messages = [{"role": "system", "content": system_message}]
for msg in history:
messages.extend([
{"role": "user", "content": msg[0]},
{"role": "assistant", "content": msg[1]}
])
messages.append({"role": "user", "content": message})
response = ""
for msg in self.client.chat_completion(
messages,
max_tokens=512,
stream=True,
temperature=0.7,
):
token = msg.choices[0].delta.content
response += token
yield response
def create_chat_interface():
agent = ChatAgent()
with gr.Blocks(theme=gr.themes.Soft()) as demo:
chatbot = gr.Chatbot(
label="SletcherSystems Sales Assistant",
height=400
)
msg = gr.Textbox(label="Type your message here...")
clear = gr.Button("Clear")
# Add initial greeting
def show_greeting():
return [[None, agent.generate_initial_greeting()]]
def respond(message, chat_history):
bot_message = ""
for chunk in agent.get_response(message, chat_history):
bot_message = chunk
yield chat_history + [[message, bot_message]]
msg.submit(respond, [msg, chatbot], [chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
# Show initial greeting
demo.load(show_greeting, None, chatbot)
return demo
if __name__ == "__main__":
demo = create_chat_interface()
demo.launch() |